Text by Trevor Keenan

Our new paper, “Global leaf trait estimates biased due to plasticity in the shade” (link to full text) was published online in Nature Plants on December 19th. It highlights a literally shady issue in plant science that has in some cases led to the underestimation of plant rates of growth and photosynthesis, among other traits.

The paper uses recent understanding of how leaf traits vary in response to light, along with a variety of global databases, to estimate trait values in fully sunlit conditions. It finds that a large proportion of trait values in current databases are significantly lower than our estimates, indicating that they were actually measured in the shade. As a result, global plant databases and models may require updating to better account for plant responses to full-sun conditions.

This issue may stem from a common tendency in fieldwork to report leaf measurements taken in partially shaded conditions as existing in more fully sunlit conditions. Often when researchers are in the field, it’s hard to get to leaves at the top of trees, particularly in densely vegetated areas such as tropical forests where the canopies can reach over 100 feet in height. In other cases, understory plants grow mostly in the shade, so it is impossible to sample in full sun. Traits vary quite a lot in the canopy, so if you don’t sample from the top all of your samples will be biased.

Large light-dependent variations in leaf traits

In plant fieldwork, full-sun conditions are defined as those in which a plant receives the maximum amount of sunlight, typically at the top of a canopy, but most leaves do not grow in full-sun conditions.

Leaves at the bottom of the canopy in a tropical rainforest may receive 100 times less sunlight than those at the top of the canopy. And many leaf characteristics—which are integral to vital leaf functions such as carbon uptake and photosynthesis—can vary 20-fold between the top and bottom leaves on the same plant. For example, the highest concentration in nitrogen is at the top, where you have the most sunlight. Plants allocate a lot of nutrients there so they can ‘profit’ from it the most.

Cutting to the root of a data problem

Together with Ülo Niinemets, we evaluated leaf data from several databases—covering hundreds of plant species and spanning most regions of the world. We used data from those studies that reported extra information about the specific location of the sampled leaves in the canopy as a benchmark for other studies’ data.

The misreported sun vs. shade conditions are likely most pronounced in tropical regions. Because these regions of tropical vegetation are also considered to be the planet’s largest “carbon sinks” in removing carbon dioxide from the atmosphere, these are some of the most important areas to focus on.

Better accounting of light conditions that sampled leaves are growing in could help to improve models that account for plants’ total rate of photosynthesis and better quantify their role as a carbon sink, for example, and for plants’ adaptability to changing conditions. It can also identify important correlations between which plant traits are most pronounced under different lighting conditions.

More accurate sampling methods can ultimately help improve scientists’ understanding of whole ecosystem structure and function, and to understand how plants respond to factors such as climate change.In addition to improved reporting of sunlit conditions, there is also a need for better accounting of plant ages in field studies, as age may affect leaf chemistry and function, according to the study.

We conclude that field studies must take more care in accurately reporting sunlit vs. shaded conditions and age-driven trait responses in leaves.

New techniques are emerging to improve data collection in the field. The study notes that some field research has used a shotgun approach to sample leaves at the top of the canopy—firing a shotgun to clip off leaves that are otherwise out of reach—though this technique alters the water flow that exists in attached leaves, so it can affect photosynthesis measurements.

LIDAR, a laser-based mapping technology, has found more use in plant field work, by providing 3-D images of forest structure, for example, and physics-based computer simulations are improving in their ability to model how leaves transfer energy from sunlight. There is definitely a path forward in technological and scientific advances, along with new measurement approaches.

The study of leaf functional trait relationships, the so-called leaf economics spectrum, is based on the assumption of high-light conditions (as experienced by sunlit leaves). Owing to the exponential decrease of light availability through canopies, however, the vast majority of the world’s vegetation exists in at least partial shade. Plant functional traits vary in direct dependence of light availability, with different traits varying to different degrees, sometimes in conflict with expectations from the economic spectrum. This means that the derived trait relationships of the global leaf economic spectrum are probably dependent on the extent to which observed data in existing large-scale plant databases represent high-light conditions. Here, using an extensive worldwide database of within-canopy gradients of key physiological, structural and chemical traits, along with three different global trait databases, we show that: (1) accounting for light-driven trait plasticity can reveal novel trait relationships, particularly for highly plastic traits (for example, the relationship between net assimilation rate per area (Aa) and leaf mass per area (LMA)); and (2) a large proportion of leaf traits in current global plant databases reported as measured in full sun were probably measured in the shade. The results show that even though the majority of leaves exist in the shade, along with a large proportion of observations, our current understanding is too focused on conditions in the sun.